The genome of an individual defines phenotypic traits (i.e., observable traits) for a staggering array of characteristics ranging from eye color to muscle tone to dietary sensitivity. Specifically, the expression of genes in various forms of Deoxyribonucleic Acid (DNA), Ribonucleic Acid (RNA), and protein may result in a specific phenotype being expressed by an individual. Even portions of the genome which are not directly linked to known phenotypic traits may provide valuable insights. For example, these portions of the genome may be used to determine the ancestry of an individual. Genetic factors therefore play a major role in defining how the human body functions, and also in defining variations between individuals.
While biological inheritance of physical traits has been studied for decades, associating specific phenotypes with specific nucleotide sequences or combinations thereof remains a complicated process. The human genome itself occupies approximately eighty Gigabytes (GB) of data. Furthermore, there are estimated to be roughly ten million Single Nucleotide Polymorphisms (SNPs) within the genome. Large stretches of the genome include non-coding regions (e.g., introns) as well as coding regions (e.g., exons), and the non-coding regions may regulate how one or more coding regions are expressed. Thus, in addition to coding regions, even variations in non-coding regions may have an impact on phenotype, and false positives may occur when attempting to determine the combination of nucleotide sequences that result in a specific phenotype. For example, genes that result in expression of a first trait (e.g., hair color) may be expressed more readily in one population than another. That population may also have genes that result in expression of additional traits. The genes that regulate expression of the additional traits are therefore highly correlated with the genes that regulate expression of the first trait. This means that there is a chance of mistakenly assuming that the genes which regulate expression of the additional traits are used to regulate expression of the first trait. For at least these reasons, the process of correlating specific variations in the genome with specific phenotypes for a characteristic can be fiendishly complex.
Further increasing the complexity of this process, it is not possible to identify many traits of an individual without studying the individual closely, and some traits may be hard to precisely quantify (e.g., hair curl, personality, etc.). Other traits may be hard to identify based on information currently known about the individual. For example, an individual who has constant headaches may be suffering from high blood pressure, high stress, allergies, or other conditions. Without more information, it would be impossible to determine which nucleotide sequences within that individual are correlated with (and/or contribute to) the reported traits.
Models have been built which attempt to predict the traits of an individual based on genetic records for that individual. However, the creation of such models is a time-consuming and labor-intensive process. This disincentivizes many entities from providing trait prediction services that are in-depth and scientifically robust. Thus, a bewildering array of entities are currently providing trait prediction services that have varying or unknown levels of quality. Hence, individuals may find themselves intimidated by the untamed ecosystem for trait prediction. Even those individuals who move forward with trait prediction need to subscribe to multiple services in order to receive predictions for all desired traits. This necessitates the transmission and sharing of genetic records across a wide number of entities, which increases the risk of genetic records being stolen or accessed without authorization.
Hence, those who desire personalized trait predictions continue to seek out enhanced systems and methods for achieving these goals.